Money Laundering Practices And Impact On Economy- Ken Research

Research report on “The Impact of Money Laundering on Customer Due Diligence” highlights, money laundering is the process by which people disguise the proceeds from criminal conduct by making them appear as proceeds from legitimate source. In India this conversion of black money into white money is known as Hawala mechanism. Money laundering generally involves three stages:

Placement: It involves depositing the illegal cash into the financial system.

Layering: This stage involves hiding the origin of funds by moving them through a series of financial transactions so that it becomes difficult to trace or detect.

Integration: This stage involves placing the illegal money into the economy through the banking system or legitimate source.

Customer due diligence is the analysis of the customers in context of legal issues like anti money laundering and also regulatory compliance. The important regulatory measures that drive CDD are The 4th AML Directive and Financial Action Task Force (FATF) Recommendations and Membership. The FATF is an inter-governmental body established in 1989 by the Ministers of its Member jurisdictions. The aim of the FATF is to set standards and to promote effective implementation of legal, regulatory and operational measures for keeping a check on money laundering, terrorist financing and the financing of proliferation and other related threats to the integrity of the international financial system.

Ken research has announced new research “The Impact of Money Laundering on Customer Due Diligence” on the impact of money laundering on customer due diligence which talks about the 4th money laundering directive which was passed by EU. The report gives the detailed analysis of the key operational and technological trends and challenges that institutions face due to the rapid rise in money laundering activity and frequent regulatory upgrades. Customer due diligence (CDD) also known as Know Your Customer (KYC) is central to an effective anti-money laundering and counter-terrorism financing (AML/CTF) regime. It is important to identify and verify each customer so as to determine the money laundering risk posed by the customer and also to determine whether to build the business relationship or not. According to the 4th money laundering directive simplified Customer Due Diligence will no longer be applicable in most circumstances. All transactions/clients require a degree of risk assessment to demonstrate that it presents a lower degree of risk and requires sufficient ongoing monitoring. There will be no blanket exemption from CDD. Transactions where enhanced due diligence (higher risk) will be required include those involving asset holding vehicles and cash-intensive businesses, those where unusual or apparently unnecessarily complex ownership structures are in place. Thus, strict rules are placed to avoid illegal activities.

Historically AML models relied on qualitative methods like expert judgment. However today, these models are integrated with sophisticated scoring algorithms and have other quantitatively derived components, such as segments and thresholds. These techniques have helped the AML industry to grow and provide accurate results. AML models include transaction monitoring software vendor products, large homegrown transaction monitoring systems, customer risk rating models and alert risk scoring models. These methods help in effective customer due diligence.

Despite all these models and methods of doing effective customer due diligence, it would be a challenging task for the nations to prevent money laundering. Risk of cyber threats and cybercrimes continue to rise. The rise of financial technology presents the challenge as the services of online money payment methods like bitcoin, paytm escalates the movement of funds become easier and quicker. As accessibility to global payments increases, the money laundering risk in the industry will similarly escalate and require institutions to implement heightened controls. Also, the markets are becoming more integrated and hence tracing the transactions becomes difficult. However, technology in this case will play an important role in managing money laundering and checking the customer’s background. Artificial intelligence and machine learning can support AML and financial crime compliance. As technology becomes more and more complex, one needs to understand not only the challenges presented by increased globalization, but also the opportunities to better understand and manage risk with enhanced solutions.

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